The present invention relates to anatomical landmark detection in medical images, and more particularly, to anatomical landmark detection in medical images using deep neural networks.
Deep learning mimics the behavior of mammal brains in order to extract a meaningful representation from a high dimensional input. Data is passed through multiple layers of a network. The primary layers extract low-level cues, such as edges and corners for natural images. Deeper layers compose simple cues from previous layers into higher-level features. In this way, powerful representations emerge at the end of the network. The gradual construction of a deep network prevents the learning from be exposed to a high complexity of data too early. Several theoretical works show that certain classes of functions (e.g., indicator function) could be represented by a deep network, but require exponential computation for a network with insufficient depth.
Recently, deep learning has been applied with high accuracy to pattern recognition problems in images. Deep neural networks can be used in image related tasks, such as detection and segmentation. However, due to high computational costs during the evaluation phase, the computation time for deep neural network networks can be prohibitively large can prevents deep neural networks from being applied to many useful applications.